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      Risk assessment models for venous thromboembolism in hospitalised adult patients: a systematic review

      systematic-review

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          Abstract

          Introduction

          Hospital-acquired thrombosis accounts for a large proportion of all venous thromboembolism (VTE), with significant morbidity and mortality. This subset of VTE can be reduced through accurate risk assessment and tailored pharmacological thromboprophylaxis. This systematic review aimed to determine the comparative accuracy of risk assessment models (RAMs) for predicting VTE in patients admitted to hospital.

          Methods

          A systematic search was performed across five electronic databases (including MEDLINE, EMBASE and the Cochrane Library) from inception to February 2021. All primary validation studies were eligible if they examined the accuracy of a multivariable RAM (or scoring system) for predicting the risk of developing VTE in hospitalised inpatients. Two or more reviewers independently undertook study selection, data extraction and risk of bias assessments using the PROBAST (Prediction model Risk Of Bias ASsessment Tool) tool. We used narrative synthesis to summarise the findings.

          Results

          Among 6355 records, we included 51 studies, comprising 24 unique validated RAMs. The majority of studies included hospital inpatients who required medical care (21 studies), were undergoing surgery (15 studies) or receiving care for trauma (4 studies). The most widely evaluated RAMs were the Caprini RAM (22 studies), Padua prediction score (16 studies), IMPROVE models (8 studies), the Geneva risk score (4 studies) and the Kucher score (4 studies). C-statistics varied markedly between studies and between models, with no one RAM performing obviously better than other models. Across all models, C-statistics were often weak (<0.7), sometimes good (0.7–0.8) and a few were excellent (>0.8). Similarly, estimates for sensitivity and specificity were highly variable. Sensitivity estimates ranged from 12.0% to 100% and specificity estimates ranged from 7.2% to 100%.

          Conclusion

          Available data suggest that RAMs have generally weak predictive accuracy for VTE. There is insufficient evidence and too much heterogeneity to recommend the use of any particular RAM.

          PROSPERO registration number

          Steve Goodacre, Abdullah Pandor, Katie Sworn, Daniel Horner, Mark Clowes. A systematic review of venous thromboembolism RAMs for hospital inpatients. PROSPERO 2020 CRD42020165778. Available from https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=165778 https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=165778

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          Most cited references74

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          Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement.

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            Applied Logistic Regression

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              PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies

              Clinical prediction models combine multiple predictors to estimate risk for the presence of a particular condition (diagnostic models) or the occurrence of a certain event in the future (prognostic models). PROBAST (Prediction model Risk Of Bias ASsessment Tool), a tool for assessing the risk of bias (ROB) and applicability of diagnostic and prognostic prediction model studies, was developed by a steering group that considered existing ROB tools and reporting guidelines. The tool was informed by a Delphi procedure involving 38 experts and was refined through piloting. PROBAST is organized into the following 4 domains: participants, predictors, outcome, and analysis. These domains contain a total of 20 signaling questions to facilitate structured judgment of ROB, which was defined to occur when shortcomings in study design, conduct, or analysis lead to systematically distorted estimates of model predictive performance. PROBAST enables a focused and transparent approach to assessing the ROB and applicability of studies that develop, validate, or update prediction models for individualized predictions. Although PROBAST was designed for systematic reviews, it can be used more generally in critical appraisal of prediction model studies. Potential users include organizations supporting decision making, researchers and clinicians who are interested in evidence-based medicine or involved in guideline development, journal editors, and manuscript reviewers.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2021
                29 July 2021
                : 11
                : 7
                : e045672
                Affiliations
                [1 ]departmentScHARR , The University of Sheffield , Sheffield, UK
                [2 ]departmentBarts and The London School of Medicine and Dentistry , Queen Mary University of London , London, UK
                [3 ]departmentDepartment of Clinical and Biomedical Sciences , University of Bolton , Bolton, UK
                [4 ]departmentDepartment of Haematology , Guy's and St Thomas' NHS Foundation Trust , London, UK
                [5 ]departmentDepartment of Medicine , McMaster University , Hamilton, Ontario, Canada
                [6 ]departmentEmergency Department , Salford Royal NHS Foundation Trust , Salford, UK
                Author notes
                [Correspondence to ] Abdullah Pandor; a.pandor@ 123456sheffield.ac.uk
                Author information
                http://orcid.org/0000-0003-2552-5260
                http://orcid.org/0000-0003-0803-8444
                http://orcid.org/0000-0003-2976-7523
                http://orcid.org/0000-0003-2763-6474
                http://orcid.org/0000-0002-0400-2017
                Article
                bmjopen-2020-045672
                10.1136/bmjopen-2020-045672
                8323381
                34326045
                e2c3377f-ed80-411f-83e0-98094ac6450b
                © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See:  https://creativecommons.org/licenses/by/4.0/.

                History
                : 12 October 2020
                : 23 June 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000664, Health Technology Assessment Programme;
                Award ID: 127454
                Categories
                Haematology (Incl Blood Transfusion)
                1506
                1700
                Original research
                Custom metadata
                unlocked

                Medicine
                vascular medicine,haematology,anticoagulation,quality in health care
                Medicine
                vascular medicine, haematology, anticoagulation, quality in health care

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